count plot for each categorical variable

I have a dataset as below, where Q1,Q2,Q3 are categorical.

  Q1,Q2,Q3
0    4,1,5
1    1,5,1
2    1,2,1
3    1,4,1
4    5,1,5

How can I plot the x axis for each column, and y as the count of the value for each column, all in one plot.

Sample out put

Answer

You can use value_counts on the columns and then plot:

# grouped by quartile
df.apply(pd.Series.value_counts).T.plot.bar()

# grouped by value
df.apply(pd.Series.value_counts).plot.bar()

old answer

A quick way using pandas only is:

df.groupby(level=0, axis=1).plot.bar()

But this won’t be flexible for the colors/layout.

If you want a nice output, rework your dataframe as long format and use seaborn.catplot:

import seaborn as sns
df2 = (df.unstack()
         .reset_index()
         .rename(columns={'level_0': 'quartile',
                          'level_1': 'index',
                          0: 'value'})
      )
sns.catplot(data=df2, col='quartile', x='index', y='value', kind='bar')

output:

I used the following input:

   Q1  Q2  Q3
0   4   1   5
1   1   5   1
2   1   2   1
3   1   4   1
4   5   1   5

combining both answers:

import seaborn as sns
df2 = (df.apply(pd.Series.value_counts)
         .unstack()
         .reset_index()
         .rename(columns={'level_0': 'quartile',
                          'level_1': 'index',
                          0: 'counts'})
      )
sns.catplot(data=df2, col='quartile', x='index', y='counts', kind='bar')